Recent advances in implicit neural representations have achieved impressive results by sampling and fusing individual points along sampling rays in the sampling space. However, due to the explosively growing sampling space, finely representing and synthesizing detailed textures remains a challenge for unbounded large-scale outdoor scenes. To alleviate the dilemma of using individual points to perceive the entire colossal space, we explore learning the surface distribution of the scene to provide structural priors and reduce the samplable space and propose a Point Diffusion implicit Function, PDF, for large-scale scene neural representation. The core of our method is a large-scale point cloud super-resolution diffusion module that enhances the sparse point cloud reconstructed from several training images into a dense point cloud as an explicit prior. Then in the rendering stage, only sampling points with prior points within the sampling radius are retained. That is, the sampling space is reduced from the unbounded space to the scene surface. Meanwhile, to fill in the background of the scene that cannot be provided by point clouds, the region sampling based on Mip-NeRF 360 is employed to model the background representation. Expensive experiments have demonstrated the effectiveness of our method for large-scale scene novel view synthesis, which outperforms relevant state-of-the-art baselines.

翻译：近期，隐式神经表示通过在采样空间中沿采样射线采样并融合单个点，取得了令人瞩目的成果。然而，由于采样空间的爆炸式增长，对于无边界的大规模户外场景，精细表示和合成细节纹理仍是一项挑战。为缓解使用单个点感知整个庞大空间的困境，我们探索学习场景的表面分布，以提供结构先验并缩减可采样空间，进而提出一种用于大规模场景神经表示的点扩散隐式函数（PDF）。该方法的核心是一个大规模点云超分辨率扩散模块，它将从若干训练图像重建的稀疏点云增强为稠密点云，作为显式先验。在渲染阶段，仅保留采样半径内具有先验点的采样点，即将采样空间从无界空间缩减至场景表面。同时，为填补无法由点云提供的场景背景，采用基于Mip-NeRF 360的区域采样来建模背景表示。大量实验证明了该方法在大规模场景新视角合成中的有效性，其性能超越了相关的最先进基线模型。